Online monitoring data acquisition and processing method and system based on water flow

Abstract

The invention discloses an online monitoring data acquisition and processing system based on water flow. The system comprises a data acquisition module, a monitoring center station, a data holder, a data transmission module and a plurality of monitoring modules, monitoring data are obtained through the multiple monitoring modules and collected and transmitted to the monitoring center station through the data collection module and the data transmission module, the water flow is obtained through real-time calculation of the monitoring center station and synchronously uploaded to the data holder for storage, and a statistical report is generated. The flow velocity data and the water level data of the water body are collected in real time through the multiple monitoring modules, meanwhile, the calculation precision of the water body flow is guaranteed through data preprocessing, real-time online monitoring of the water body flow is achieved, a user can know dynamic changes of the water body conveniently, and early warning information is sent out when the water body flow exceeds an early warning value.

Description

Technical Field

[0001] The present invention belongs to the technical field of water flow monitoring, and in particular relates to a method and system for collecting and processing data based on online monitoring of water flow. Background Art

[0002] Water flow can change significantly in a short period of time due to the influence of rainfall, and some lakes, rivers and other water bodies are prone to flood disasters after changes in water flow, so real-time monitoring of water flow is necessary.

[0003] With the widespread application of new flow measurement sensor technology, the accuracy, stability and reliability of flow measurement have been greatly improved. However, when combining the Internet of Things and big data to analyze water flow, there are still large errors in the processing and analysis of collected data. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to address the deficiencies of the above-mentioned existing technologies and provide a method and system for collecting and processing water flow online monitoring data, which can effectively process the collected monitoring data and effectively improve the accuracy of real-time feedback from the water flow monitoring platform.

[0005] The technical solution adopted by the present invention is: a data acquisition and processing system based on online monitoring of water flow, including a data acquisition module, a monitoring center station, a data pan / tilt station, a data transmission module and multiple monitoring modules;

[0006] The plurality of monitoring modules include a plurality of flow rate sensors and a plurality of water level gauges, the flow rate sensors are used to measure the flow rate of the water body, and the water level gauges are used to measure the water level;

[0007] The data acquisition module is used to collect monitoring data output by the monitoring module;

[0008] The monitoring center station is used to receive monitoring data, pre-process the monitoring data, obtain water flow data in real time through the pre-processed monitoring data, and issue an early warning when the water flow is abnormal;

[0009] The data cloud platform is used to receive and store water flow data uploaded by the detection center station, and analyze the water flow data to generate statistical reports.

[0010] Preferably, the monitoring data preprocessing comprises the following steps:

[0011] S1: Initial screening of monitoring data based on physical thresholds;

[0012] S2: Perform dynamic threshold anomaly detection on the monitoring data after initial screening, delete outliers and perform interpolation replacement;

[0013] S3: Linear interpolation is performed to fill in short-term missing data based on time series.

[0014] Preferably, the multiple monitoring modules are jointly calibrated using a Bayesian network, and the water level data and flow rate data are input into a probability graph model. After iteratively correcting the confidence weights, the optimal water level estimation and the optimal flow rate estimation are obtained.

[0015] Preferably, the acquired water flow data includes water flow data of regular river sections and water flow data of irregular river sections.

[0016] Preferably, the acquisition of water flow data of the irregular river section comprises the following steps:

[0017] A1: Dynamically generate cross-section mesh using water level data changes:

[0018] Obtain the river cross-section baseline coordinate set S={(x i ,z i )}, where x i is the horizontal distance, z i is the riverbed elevation;

[0019] Determine the current water flow section range according to the real-time water level H, and the water flow area = {(x i ,z i )|z i ≤H}, remove z i >H invalid point, generate the current valid cross-section profile;

[0020] The non-uniform segmentation method is used to divide the sub-regions according to the curvature change;

[0021] Horizontal division: along the x-axis, increase the node density at the point where the cross-section width suddenly changes;

[0022] Vertical stratification: by water depth h = Hz i Layered, with each layer height increasing as water depth increases;

[0023] A2: Combine the velocity data measured at limited points and reconstruct the velocity field of the entire cross section through interpolation algorithm:

[0024] Assume the sensor is at position x j The vertical velocity v at different depths is measured j,k (k=1,2,……n), fitting the vertical velocity distribution curve, and obtaining the vertical velocity distribution through the exponential law model,

[0025]

[0026] Where m is a coefficient, which is adjusted according to the roughness of the river channel;

[0027] For the lateral position x where no sensor is arranged, the inverse distance weighted method is used to estimate the surface velocity v s (x)

[0028]

[0029] Where p is the attenuation factor and N is the number of adjacent measuring points;

[0030] Combine the vertical velocity distribution and the lateral interpolation results to generate the velocity distribution of each grid cell (x i ,h k ) flow rate v i,k ;

[0031] Finally, the cross-sectional shape factor is introduced to correct the integral error and obtain the water flow data of the irregular river section.

[0032] Preferably, a processing method based on a water flow online monitoring data acquisition and processing system comprises the following steps:

[0033] M1: Through the setting of multiple monitoring modules, real-time monitoring is performed to obtain water flow rate data and water level data at the corresponding location;

[0034] M2: Perform preliminary screening of water velocity data and water level data based on physical thresholds, perform dynamic threshold anomaly detection on the monitored data after preliminary screening, delete outliers and perform interpolation replacement, and finally perform linear interpolation to fill in short-term missing water velocity data and water level data based on time series;

[0035] M3: The water flow data is obtained by calculating the pre-processed water velocity data and water level data, thereby realizing real-time online monitoring of the water flow data. When the water flow data exceeds the set threshold, an early warning message is issued.

[0036] The beneficial effects of the present invention are: the present invention collects water flow rate data and water level data in real time through multiple monitoring modules, and at the same time ensures the calculation accuracy of water flow through data preprocessing, realizes real-time online monitoring of water flow, makes it convenient for users to understand the dynamic changes of water bodies, and issues warning information when the water flow exceeds the warning value. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0038] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] Example

[0040] like Figure 1As shown, this embodiment provides a data acquisition and processing system based on online monitoring of water flow, including a data acquisition module, a monitoring center station, a data pan / tilt station, a data transmission module and multiple monitoring modules;

[0041] The multiple monitoring modules include several flow rate sensors and several water level meters. The flow rate sensors are used to measure the flow rate of the water body and obtain water flow rate data. The water level meters are used to measure the water level and obtain water level data. The multiple monitoring modules are jointly calibrated using a Bayesian network. The water level data and flow rate data are input into a probability graph model. After iteratively correcting the confidence weights, the optimal water level estimation and the optimal flow rate estimation are obtained.

[0042] The data acquisition module is used to collect the water flow rate data and water level data output by the monitoring module; after the data is collected, the data is transmitted through the data transmission module;

[0043] The monitoring center station is used to receive monitoring data, that is, to receive water flow rate data and water level data, and to pre-process the water flow rate data and water level data after receiving the data. The pre-processing includes the following steps:

[0044] S1: Initial screening of monitoring data based on physical thresholds;

[0045] S2: Perform dynamic threshold anomaly detection on the monitoring data after initial screening, delete outliers and perform interpolation replacement;

[0046] S3: Linear interpolation is performed to fill in short-term missing data based on time series;

[0047] The pre-processed water velocity data and water level data are used to calculate water flow data. For regular river sections, the flow results are calculated using conventional mathematical formulas. For irregular river sections, the water flow data acquisition includes the following steps:

[0048] A1: Dynamically generate cross-section mesh using water level data changes:

[0049] Obtain the river cross-section baseline coordinate set S={(x i ,z i )}, where x i is the horizontal distance, z i is the riverbed elevation;

[0050] Determine the current water flow section range according to the real-time water level H, and the water flow area = {(x i ,z i )|z i ≤H}, remove z i >H invalid point, generate the current valid cross-section profile;

[0051] The non-uniform segmentation method is used to divide the sub-regions according to the curvature change;

[0052] Horizontal division: along the x-axis, increase the node density at the point where the cross-section width suddenly changes;

[0053] Vertical stratification: by water depth h = Hz i Layered, with each layer height increasing as water depth increases;

[0054] A2: Combine the velocity data measured at limited points and reconstruct the velocity field of the entire cross section through interpolation algorithm:

[0055] Assume the sensor is at position x j The vertical velocity v at different depths is measured j,k (k=1,2,……n), fitting the vertical velocity distribution curve, and obtaining the vertical velocity distribution through the exponential law model,

[0056]

[0057] Where m is a coefficient, which is adjusted according to the roughness of the river channel;

[0058] For the lateral position x where no sensor is arranged, the inverse distance weighted method is used to estimate the surface velocity v s (x)

[0059]

[0060] Where p is the attenuation factor and N is the number of adjacent measuring points;

[0061] Combine the vertical velocity distribution and the lateral interpolation results to generate the velocity distribution of each grid cell (x i ,h k ) flow rate v i,k ;

[0062] Finally, the cross-sectional shape factor is introduced to correct the integration error and obtain the water flow data of the irregular river section.

[0063] That is, the monitoring center station can provide real-time feedback of water flow data online. When the water flow data changes beyond the set threshold, the monitoring center station will issue an early warning.

[0064] The monitoring data is pre-processed to obtain real-time water flow data through the pre-processed monitoring data. When the water flow is abnormal, an early warning is issued to remind relevant personnel to take measures to effectively avoid disasters such as floods and dam breaks.

[0065] The data cloud platform is used to receive and store water flow data uploaded by the detection center station, and analyze the water flow data to generate statistical reports.

[0066] The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any modification and replacement based on the technical solution and inventive concept provided by the present invention should be covered by the protection scope of the present invention.

Claims

1. A data acquisition and processing system based on online monitoring of water flow, characterized by: It includes data acquisition module, monitoring center station, data pan / tilt station, data transmission module and multiple monitoring modules; The plurality of monitoring modules include a plurality of flow rate sensors and a plurality of water level gauges, the flow rate sensors are used to measure the flow rate of the water body, and the water level gauges are used to measure the water level; The data acquisition module is used to collect monitoring data output by the monitoring module; The monitoring center station is used to receive monitoring data, pre-process the monitoring data, obtain water flow data in real time through the pre-processed monitoring data, and issue an early warning when the water flow is abnormal; The data cloud platform is used to receive and store water flow data uploaded by the detection center station, and analyze the water flow data to generate statistical reports.

2. The data acquisition and processing system based on online monitoring of water flow according to claim 1 is characterized in that: The monitoring data preprocessing comprises the following steps: S1: Initial screening of monitoring data based on physical thresholds; S2: Perform dynamic threshold anomaly detection on the monitoring data after initial screening, delete outliers and perform interpolation replacement; S3: Linear interpolation is performed to fill in short-term missing data based on time series.

3. The data acquisition and processing system based on online monitoring of water flow according to claim 1 is characterized in that: The multiple monitoring modules are jointly calibrated using a Bayesian network, and the water level data and flow rate data are input into a probability graph model. After iteratively correcting the confidence weights, the optimal water level estimation and the optimal flow rate estimation are obtained.

4. The data acquisition and processing system based on online monitoring of water flow according to claim 1 is characterized in that: The acquired water flow data includes water flow data of regular river sections and water flow data of irregular river sections.

5. The data acquisition and processing system based on online monitoring of water flow according to claim 4 is characterized in that: The acquisition of water flow data of the irregular river section comprises the following steps: A1: Dynamically generate cross-section mesh using water level data changes: Obtain the river cross-section baseline coordinate set S={(x i ,z i )}, where x i is the horizontal distance, z i is the riverbed elevation; Determine the current water flow section range according to the real-time water level H, and the water flow area = {(x i ,z i )|z i ≤H}, remove z i >H invalid point, generate the current valid cross-section profile; The non-uniform segmentation method is used to divide the sub-regions according to the curvature change; Horizontal division: along the x-axis, increase the node density at the point where the cross-section width suddenly changes; Vertical stratification: by water depth h = Hz i Layered, with each layer height increasing with water depth; A2: Combine the velocity data measured at limited points and reconstruct the velocity field of the entire cross section through interpolation algorithm: Assume the sensor is at position x j The vertical velocity v at different depths is measured j,k (k=1,2,……n), fitting the vertical velocity distribution curve, and obtaining the vertical velocity distribution through the exponential law model, Where m is a coefficient, which is adjusted according to the roughness of the river channel; For the lateral position x where no sensor is arranged, the inverse distance weighted method is used to estimate the surface velocity v s (x) Where p is the attenuation factor and N is the number of adjacent measuring points; Combine the vertical velocity distribution and the lateral interpolation results to generate the velocity distribution of each grid cell (x i ,h k ) flow rate v i,k ; Finally, the cross-sectional shape factor is introduced to correct the integral error and obtain the water flow data of the irregular river section.

6. A processing method based on a water flow online monitoring data acquisition and processing system according to claims 1-5, comprising the following steps: M1: Through the setting of multiple monitoring modules, real-time monitoring is performed to obtain water flow rate data and water level data at the corresponding location; M2: Perform preliminary screening of water velocity data and water level data based on physical thresholds, perform dynamic threshold anomaly detection on the monitored data after preliminary screening, delete outliers and perform interpolation replacement, and finally perform linear interpolation to fill in short-term missing water velocity data and water level data based on time series; M3: The water flow data is obtained by calculating the pre-processed water velocity data and water level data, thereby realizing real-time online monitoring of the water flow data. When the water flow data exceeds the set threshold, an early warning message is issued.

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